Systems and methods for detecting camera defect caused by exposure to radiation

ABSTRACT

A method of detecting camera defect includes: obtaining an image by a processing unit, the processing unit having a surface fit module, a subtraction module, and a peak quantification module; determining a first autocorrelation map for a first sub-region in the image; determining, using the surface fit module, a first surface fit for first scene content in the first sub-region; subtracting, using the subtraction module, the first surface fit from the first autocorrelation map for the first sub-region in the image to obtain a first residual map; and quantifying, using the peak quantification module, a first noise in the first residual map.

FIELD

The field of the application relates to cameras for use in medicalprocedures, and more particularly, to systems and methods for detectingcamera defect caused by exposure to radiation.

BACKGROUND

Radiation therapy involves medical procedures that selectively exposecertain areas of a human body, such as cancerous tumors, to high dosesof radiation. Radiation may also be used to obtain images of the patientduring an imaging procedure.

In some medical procedures involving use of radiation, one or morecameras may be set up to perform one or more functions during themedical procedures. For example, a camera may be used to monitor aposition of a patient as the patient is being treated by radiation. Thecamera may also be used to determine a breathing phase of a patient asthe patient is being treated by treatment radiation, and/or imaged bydiagnostic radiation. In some cases, one or more cameras may be used todetect possible collision between a treatment machine and the patient.

Camera used with radiation procedure is exposed to radiation, which candamage the camera gradually over time.

In accordance with one or more embodiments described herein, aquantitative measure of the camera defect and an automatic imageanalysis method that generates this quantitative measure from cameraimages (e.g., those from a live video stream) are provided. Suchtechnique is advantageous because the measure may be obtained withoutrequiring a user to run regularly scheduled tests for the camera.

SUMMARY

A method of detecting camera defect includes: obtaining an image by aprocessing unit, the processing unit having a surface fit module, asubtraction module, and a peak quantification module; determining afirst autocorrelation map for a first sub-region in the image;determining, using the surface fit module, a first surface fit for firstscene content in the first sub-region; subtracting, using thesubtraction module, the first surface fit from the first autocorrelationmap for the first sub-region in the image to obtain a first residualmap; and quantifying, using the peak quantification module, a firstnoise in the first residual map.

Optionally, the act of determining the surface fit comprises determininga planar surface that represents the first scene content.

Optionally, the act of determining the surface fit comprises determininga non-planar surface that represents the first scene content.

Optionally, the act of quantifying the first noise comprises determiningwhether a magnitude of the first noise exceeds a threshold or not.

Optionally, the method further includes determining a level ofconfidence associated with the quantified first noise.

Optionally, the act of determining the level of confidence comprisesdetermining a standard deviation associated with a residual sidelobe inthe first residual map.

Optionally, the method further includes displaying the image in ascreen, and displaying an indicator representing the quantified firstnoise in the screen.

Optionally, the indicator has a first color if the quantified firstnoise is above a threshold, and a second color if the quantified firstnoise is below the threshold.

Optionally, the indicator has a first color if the quantified firstnoise is above a threshold, and the quantified first noise is determinedwith confidence, the indicator has a second color if the quantifiedfirst noise is below the threshold, and the quantified first noise isdetermined with confidence, and the indicator has a third color if thequantified first noise cannot be determined with confidence.

Optionally, the indicator is displayed over the image, and is at alocation that corresponds with a position of the sub-region in theimage.

Optionally, the indicator comprises a dot.

Optionally, the method further includes determining a secondautocorrelation map for a second sub-region in the image; determining asecond surface fit for second scene content in the second sub-region;subtracting the second surface fit from the second autocorrelation mapfor the second sub-region in the image to obtain a second residual map;and quantifying a second noise in the second residual map.

Optionally, the method further includes: obtaining an additional image;determining a second autocorrelation map for a second sub-region in theadditional image; determining a second surface fit for second scenecontent in the second sub-region; subtracting the second surface fitfrom the second autocorrelation map for the second sub-region in theimage to obtain a second residual map; and quantifying a second noise inthe second residual map; wherein the first image and the additionalimage are obtained at different respective times; and wherein a positionof the first sub-region with respect to the image is a same as aposition of the second sub-region with respect to the additional image.Optionally the method is applied to a time average of an image sequence.Time averaging may be recursive or block (boxcar) average.

Optionally, the method further includes presenting the quantified firstnoise and the quantified second noise as a function of time.

An apparatus for detecting camera defect includes: a processing unithaving a surface fit module, a subtraction module, and a peakquantification module; wherein the processing unit is configured toobtain an image, and determining a first autocorrelation map for a firstsub-region in the image; wherein the surface fit module is configured todetermine a first surface fit for first scene content in the firstsub-region; wherein the subtraction module is configured to subtract thefirst surface fit from the first autocorrelation map for the firstsub-region in the image to obtain a first residual map; and wherein thepeak quantification module is configured to quantify a first noise inthe first residual map.

Optionally, the surface fit module is configured to determine thesurface fit by determining a planar surface that represents the firstscene content.

Optionally, the surface fit module is configured to determine thesurface fit by determining a non-planar surface that represents thefirst scene content.

Optionally, the peak quantification module is configured to quantify thefirst noise by determining whether a magnitude of the first noiseexceeds a threshold or not.

Optionally, the apparatus further includes a confidence level moduleconfigured to determine a level of confidence associated with thequantified first noise.

Optionally, the confidence level module is configured to determine thelevel of confidence by determining a standard deviation associated witha residual sidelobe in the first residual map.

Optionally, the apparatus further includes a screen for displaying theimage, and for displaying an indicator representing the quantified firstnoise.

Optionally, the indicator has a first color if the quantified firstnoise is above a threshold, and a second color if the quantified firstnoise is below the threshold.

Optionally, the indicator has a first color if the quantified firstnoise is above a threshold, and the quantified first noise is determinedwith confidence, the indicator has a second color if the quantifiedfirst noise is below the threshold, and the quantified first noise isdetermined with confidence, and the indicator has a third color if thequantified first noise cannot be determined with confidence.

Optionally, the screen is configured to display the indicator over theimage at a location that corresponds with a position of the sub-regionin the image.

Optionally, the indicator comprises a dot.

Optionally, the processing unit is configured to determine a secondautocorrelation map for a second sub-region in the image; wherein thesurface fit module is configured to determine a second surface fit forsecond scene content in the second sub-region; wherein the subtractionmodule is configured to subtract the second surface fit from the secondautocorrelation map for the second sub-region in the image to obtain asecond residual map; and wherein the peak quantification module isconfigured to quantify a second noise in the second residual map.

Optionally, the processing unit is configured to obtain an additionalimage, and determine a second autocorrelation map for a secondsub-region in the additional image; wherein the surface module isconfigured to determine a second surface fit for second scene content inthe second sub-region; wherein the subtraction module is configured tosubtract the second surface fit from the second autocorrelation map forthe second sub-region in the image to obtain a second residual map; andwherein the peak quantification module is configured to quantify asecond noise in the second residual map; wherein the first image and theadditional image are obtained at different respective times; and whereina position of the first sub-region with respect to the image is a sameas a position of the second sub-region with respect to the additionalimage.

Optionally, the apparatus further includes a screen for presenting thequantified first noise and the quantified second noise as a function oftime.

A processor-program product includes a set of instruction, an executionof which by a processing unit causes a method of detecting camera defectto be performed, the method comprising: obtaining an image; determininga first autocorrelation map for a first sub-region in the image;determining, using a surface fit module, a first surface fit for firstscene content in the first sub-region; subtracting, using a subtractionmodule, the first surface fit from the first autocorrelation map for thefirst sub-region in the image to obtain a first residual map; andquantifying, using a peak quantification module, a first noise in thefirst residual map.

Other and further aspects and features will be evident from reading thefollowing detailed description.

DESCRIPTION OF THE DRAWINGS

The drawings illustrate the design and utility of embodiments, in whichsimilar elements are referred to by common reference numerals. Thesedrawings are not necessarily drawn to scale. In order to betterappreciate how the above-recited and other advantages and objects areobtained, a more particular description of the embodiments will berendered, which are illustrated in the accompanying drawings. Thesedrawings depict only exemplary embodiments and are not therefore to beconsidered limiting in the scope of the claims.

FIG. 1 illustrates a radiation treatment system configured for detectingpossible collision between a moving part of a medical device and apatient during a medical procedure in accordance with some embodiments.

FIGS. 2A-2C illustrate progression of damage to camera due to exposureto radiation.

FIG. 3 illustrates a method for detecting camera defect caused byexposure to radiation in accordance with some embodiments.

FIG. 4 illustrates an example of autocorrelation function in asub-region.

FIGS. 5A-5B illustrate examples of autocorrelation map after planar fitsubtraction for a high scene content, and a bland scene content,respectively.

FIG. 6 illustrates an example of a result obtained by using the methodof FIG. 3.

FIG. 7 illustrates noise as a function of degradation time for a cameraexposed to radiation.

FIG. 8 illustrates noise as a function of degradation time for a cameranot exposed to radiation.

FIG. 9 illustrates a specialized processing system with whichembodiments described herein may be implemented.

DETAILED DESCRIPTION

Various embodiments are described hereinafter with reference to thefigures. It should be noted that the figures are not drawn to scale andthat elements of similar structures or functions are represented by likereference numerals throughout the figures. It should also be noted thatthe figures are only intended to facilitate the description of theembodiments. They are not intended as an exhaustive description of theinvention or as a limitation on the scope of the invention. In addition,an illustrated embodiment needs not have all the aspects or advantagesshown. An aspect or an advantage described in conjunction with aparticular embodiment is not necessarily limited to that embodiment andcan be practiced in any other embodiments even if not so illustrated, orif not so explicitly described.

FIG. 1 illustrates a radiation treatment system 10. The system 10includes an arm gantry 12, a patient support 14 for supporting a patient20, and a control system 18 for controlling an operation of the gantry12 and delivery of radiation. The system 10 also includes a radiationsource 22 that projects a beam 26 of radiation towards the patient 20while the patient 20 is supported on support 14, and a collimator system24 for changing a cross sectional shape of the radiation beam 26. Theradiation source 22 may be configured to generate a cone beam, a fanbeam, or other types of radiation beams in different embodiments. Also,in other embodiments, the source 22 may be configured to generate protonbeam as a form of radiation for treatment purpose. Also, in otherembodiments, the system 10 may have other form and/or configuration. Forexample, in other embodiments, instead of an arm gantry 12, the system10 may have a ring gantry 12.

In the illustrated embodiments, the radiation source 22 is a treatmentradiation source for providing treatment energy. In other embodiments,in addition to being a treatment radiation source, the radiation source22 can also be a diagnostic radiation source for providing diagnosticenergy for imaging purpose. In such cases, the system 10 will include animager, such as the imager 80, located at an operative position relativeto the source 22 (e.g., under the support 14). In further embodiments,the radiation source 22 may be a treatment radiation source forproviding treatment energy, wherein the treatment energy may be used toobtain images. In such cases, in order to obtain imaging using treatmentenergies, the imager 80 is configured to generate images in response toradiation having treatment energies (e.g., MV imager). In someembodiments, the treatment energy is generally those energies of 160kilo-electron-volts (keV) or greater, and more typically 1mega-electron-volts (MeV) or greater, and diagnostic energy is generallythose energies below the high energy range, and more typically below 160keV. In other embodiments, the treatment energy and the diagnosticenergy can have other energy levels, and refer to energies that are usedfor treatment and diagnostic purposes, respectively. In someembodiments, the radiation source 22 is able to generate X-ray radiationat a plurality of photon energy levels within a range anywhere betweenapproximately 10 keV and approximately 20 MeV. In the illustratedembodiments, the radiation source 22 is carried by the arm gantry 12.Alternatively, the radiation source 22 may be located within a bore(e.g., coupled to a ring gantry).

In the illustrated embodiments, the control system 18 includes aprocessing unit 54, such as a processor, coupled to a control 40. Thecontrol system 18 may also include a monitor 56 for displaying data andan input device 58, such as a keyboard or a mouse, for inputting data.The operation of the radiation source 22 and the gantry 12 arecontrolled by the control 40, which provides power and timing signals tothe radiation source 22, and controls a rotational speed and position ofthe gantry 12, based on signals received from the processing unit 54.Although the control 40 is shown as a separate component from the gantry12 and the processing unit 54, in alternative embodiments, the control40 can be a part of the gantry 12 or the processing unit 54.

In some embodiments, the system 10 may be a treatment system configuredto deliver treatment radiation beam towards the patient 20 at differentgantry angles. During a treatment procedure, the source 22 rotatesaround the patient 20 and delivers treatment radiation beam fromdifferent gantry angles towards the patient 20. While the source 22 isat different gantry angles, the collimator 24 is operated to change theshape of the beam to correspond with a shape of the target tissuestructure. For example, the collimator 24 may be operated so that theshape of the beam is similar to a cross sectional shape of the targettissue structure. In another example, the collimator 24 may be operatedso that different portions of the target tissue structure receivedifferent amount of radiation (as in an IMRT procedure).

As shown in FIG. 1, the medical system 10 also includes a camera 100.The camera 100 may be used for various purposes in a medical procedure.The camera 100 is illustrated as being mounted to the patient support bya securing mechanism 132. In other embodiments, the camera 100 may bemounted to another part of the medical system 10, or to anotherstructure separate from the medical system 10 but is in the same room asthe medical system 10. Although only one camera 100 is shown in theexample, in other embodiments, the system 10 may have multiple cameras100. For example, in other embodiments, there may be multiple camerasmounted to the system 10 and/or to other structure(s) in a medical room(such as a post, a ceiling, a floor, etc.).

The camera 100 may be progressively damaged by radiation. Due to gradualdegradation, a user may adjust to, and may tolerate, the adverse visualeffect of the damage. The degradation at the camera 100 may manifest asa fixed pattern of speckle noise missing with the live image. In thecase in which the camera 100 is a color video camera, such may be causedby color mask damage in a color video camera. FIGS. 2A-2C illustrate asequence of images at increasing levels of camera degradation due toexposure to radiation. At increased levels of defect, important eventsoccurring during a medical procedure may be missed by a therapist, whomonitors the treatment room through the camera 100 on a display in thecontrol room. For example, if the camera 100 is configured to monitorpatient's movement, degradation of the camera 100 may cause a therapistand/or the processing unit (processing the images from the camera 100)to miss an important event, such as unwanted patient movements duringradiation dose delivery. In another example, if the camera 100 isconfigured to detect possible collision between a moveable part of thesystem 100 and the patient, degradation of the camera 100 may cause atherapist and/or the processing unit (processing the images from thecamera 100) unable to detect a possible collision between a machine partand the patient. In further example, if the camera 100 is used to tracka position of a fiducial or a part of a patient in a position monitoringsystem for treatment and/or diagnostic purpose, degradation of thecamera 100 may cause the position monitoring system to perform thetracking inaccurately.

In accordance with some embodiments, a quantitative measure of thecamera defect and an automatic image analysis method that generates thisquantitative measure from camera images (e.g., those from a live videostream) are provided. Such technique is advantageous because the measuremay be obtained without requiring a user to run regularly scheduledtests for the camera 100. In some embodiments, the quantitative measuremay be generated periodically. In other embodiments, the quantitativemeasure may be generated in response to a user's command. In oneimplementation, an image from the camera 100 may be divided intosub-regions. An algorithm may be utilized to analyze an autocorrelationfunction of the image in each sub-region. The goal of the algorithm isto separately quantify the local peak value in each sub-region, afterautocorrelation values due to scene content is subtracted from theautocorrelation function. The algorithm also identifies the sub-regionsfor which this subtraction can be done with high confidence. This helpslimit the noise measurement to those sub-regions only.

FIG. 3 illustrates a method 300 for detecting camera defect caused byexposure to radiation in accordance with some embodiments. In someembodiments, the method 300 may be performed by a processing unit, whichis configured to process digitized images (e.g., video frames) from thecamera 100. By means of non-limiting examples, the processing unit maybe the processing unit 54, or another processing unit. Also, theprocessing unit may be implemented using one or more processors, such asa FPGA processor, an ASIC processor, a microprocessor, a signalprocessor, a general purpose processor, or any of other types ofprocessor.

First, an image from the camera 100 is obtained (item 302). In somecases, the act of obtaining the image in item 302 may be performed by aprocessing unit, such as the processing unit 54 or another processingunit that is communicatively coupled to the camera 100. Also, in somecases, the act of obtaining the image may be performed by the camera100, which generates the image for transmission to a processing unit forprocessing. In some embodiments, the “image” may be time-average of asequence of images obtained recursively or by block (boxcar) averaging.

Next, the image is divided into a number of sub-regions for analysisindependently (item 304). In some embodiments, the image may be dividedinto a grid of sub-regions, each of which having a square or arectangular shape. In other embodiments, the sub-regions may have atriangular shape, or any of other shapes. Also, in some embodiments, thesub-regions have the same shape and size. In other embodiments, onesub-regions may have a shape and/or size that is different from anothersub-region. In one implementation, the division of the image intosub-regions may be performed by a processing unit, which may be theprocessing unit 54, or another processing unit. For example, if theimage obtained from item 302 has a rectangular shape with a width of Wand a height of H, and if the image is to be divided into a R=3 rolls byC=4 columns of sub-regions, then the processing unit may be configuredto determine the width W′ and height H′ of each sub-region asW′=W/(C−1), and H′=H/(R−1). The sub-regions may or may not overlap.

In some cases, the sub-regions in an image for analysis arepre-determined. In such cases, the act of dividing the image into thesub-regions may be performed by the processing unit selecting differentparts of the image corresponding with the sub-regions for analysis.

Next, the autocorrelation function of the image in each sub-region isdetermined and analyzed to measure the intensity of the fixed patternspeckle noise in each sub-region (item 306). In the illustratedembodiments, item 306 includes determining an autocorrelation map for asub-region in the image (item 308). The autocorrelation map isdetermined in order to 1) indicate whether in this sub-region theintensity of the speckle noise can be measured, and 2) if yes, whetherspeckle noise of significant intensity is present. FIG. 4 illustrates anexample of autocorrelation map/function R(x,y) in a sub-region. In someembodiments, R(x,y) may be determined as R(x,y)=Σ_(x′)Σ_(Y′)I(x′+x,y′+y)*I(x′,y′), where the summation of the product of the imageI(x′,y′) and its shifted version I(x′+x,y′+y) is over x′ and y′ withinthe sub-region. The purpose of this mathematical operation is to createquantitative measures that allow determination of whether the presenceof speckle noise can be detected from the sub-region scene content, andwhether the speckle noise intensity can be quantified. In some cases,the sub-region may be a template, which can for example comprise an N×Npixel sub-region. In the illustrated example, the value of local peakR(0, 0) represents the power (intensity) of the fixed pattern noise. Thewidth of the peak, which relates to how quickly it drops off frommaximum value, is proportional to the speckle noise size. Outside thearea of local peak (e.g., the sidelobe regions), the variations ofR(x,y) is mainly influenced by the spatial frequency of the scenecontent in the sub-region.

Also, as shown in the illustrated embodiments, after the autocorrelationmap is determined for a sub-region in the image, a surface fit for ascene content in the sub-region in the image is then determined (item310). The surface fit is for accounting for local scene content. Varioustechniques may be used to account for the local scene content. In onetechnique, a planar fit may be performed by the processing unit to fitto the sidelobe regions of the autocorrelation function R(x,y). Forexample, this may be done by excluding a region around (0,0) in thecorrelation map. The size of the excluded region may be determined basedon the knowledge of the noise speckle size. The planar fit is made tothe remaining points of the correlation map after this exclusion. Onemethod of planar fit is to estimate the plane coefficients whichminimize the residual correlation map values after subtracting thefitted plane. For example the RMS (root mean squared) value of theresidual values may be minimized.

In other embodiments, instead of a planar fit, the processing unit maydetermine a higher order polynomial surface, and may use it to subtractthe scene content contributing to the autocorrelation map. Accordingly,the surface fit performed by the processing unit may be planar, ornon-planar.

Next, the processing unit subtracts the plane of the planar fit from theautocorrelation function R(x,y) to obtain a residual map R′(x,y) (item312).

After the surface fit (i.e., representing autocorrelation values due toscene content) is subtracted from the autocorrelation function in asub-region, the processing unit then quantifies the local peak for thatsub-region (item 314). In some cases, the processing unit quantifies thelocal peak by determining a magnitude of the local peak representingnoise.

The processing unit is configured to perform the surface fitdetermination (item 310), surface fit subtraction (item 312), and localpeak quantification (item 314) for each of the sub-regions in the image.For example, in one implementation, the processing unit may include asurface fit module configured to determine the surface fit thatcorresponds with the scene content. The processing unit may also includea surface fit subtraction module configured to receive the surface fitand the autocorrelation function R(x,y) as inputs, and output R′(x,y),which is obtained by subtracting the surface fit from R(x,y). Theprocessing unit may also include a local peak quantification moduleconfigured to quantify the local peak.

In some cases, if the quantified noise (e.g., a magnitude of the noise)in a sub-region is less than a threshold, then the peak quantificationmodule may classify that sub-region as having “low” noise. On the otherhand, if the quantified noise in the sub-region is more than athreshold, then the peak quantification module may classify thatsub-region as having “high” noise.

In some embodiments, the processing unit also identifies the sub-regionsfor which the noise quantification can be done with high confidence.This helps limit the noise measurement to those sub-regions only. Insome cases, after the surface fit subtraction, if there is excessiveresidual sidelobe, then the noise in that sub-region cannot be measuredaccurately. FIGS. 5A-5B illustrate examples of autocorrelation map aftersurface fit subtraction for a high scene content, and a bland scenecontent, respectively. As shown in FIG. 5A, because the sub-region hashigh scene content, after determining the surface fit for thatsub-region, and after subtracting the surface fit from theautocorrelation map for that sub-region, the residual map R′(x,y) mayhave excessive residual sidelobe(s) 502. In such cases, the quantifiedpeak may not be determined with high confidence. On the other hand, asshown in FIG. 5B, if the sub-region has bland scene (low scene content),after determining the surface fit for that sub-region, and aftersubtracting the surface fit from the autocorrelation map for thatsub-region, the residual map R′(x,y) may not have any excessive residualsidelobe. In such cases, the quantified peak may be determined with highconfidence.

In one implementation, the processing unit may include a confidencelevel module configured to determine a confidence level associated witheach sub-region. In particular, the confidence level is configured todetermine, for each of the sub-regions in the image, whether there isexcessive residual sidelobe after the surface fit subtraction. If asub-region has excessive residual sidelobe, then this may then be usedto indicate that the peak for that sub-region cannot be determined withconfidence. If the contribution of scene content is successfullysubtracted from the autocorrelation map R(x,y), then the remainingsidelobe standard deviation should be mainly due to speckle noise. Undersome approximating assumptions, the standard deviation of a residualsidelobe (obtained after the surface fit subtraction) should be lessthan the peak value by a factor of M, where M is proportional to N, andwhere an N×N template is used to calculate the autocorrelation surfacemap. Thus, M is proportional to the square root of the total number ofpixels in the template. A completely “bland” scene results in perfectsurface fit wherein the noise-only threshold of PSR (peak-to-sideloberatio) may be used. In some cases, a smaller PSR threshold may be used,thereby allowing for some slow intensity variation due to scene content.

The resulting residual map R′(x,y) may then be analyzed by theprocessing unit for determining peak-to-sidelobe ratio (PSR). In somecases, the sidelobe standard deviation is simply the standard deviationof the residuals after planar fit subtraction. Also, the peak may beR(0,0) (after planar fit subtraction), which also determines the noiselevel. If the contribution of the scene content is successfullysubtracted, then the remaining sidelobe standard deviation should bemainly due to the speckle noise (resulted from the damage to the camera100).

In some embodiments, information regarding the determined noise may beoutput by the processing unit for display in a screen. FIG. 6illustrates an example of a result obtained by using the method of FIG.3. As shown in the figure, the processing unit generates multipleindicators that are displayed over the image. Each indicator is in aform of a dot, and is at a location that corresponds with a position ofthe corresponding sub-region in the image. In the illustrated example,the blue dots indicate the sub-regions where the noise could not bemeasured because of excessive residual sidelobe after the surface fitsubtraction. The green dots indicate sub-regions where noise could bemeasured (because there is no excessive residual sidelobe after thesurface fit subtraction), and the noise was less than a threshold (e.g.,2 counts standard deviation out of 256 counts for an 8-bit digitizedimage). The red dots show the sub-regions where noise could be measured(because there is no excessive residual sidelobe after the surface fitsubtraction), and the noise RMS was larger than the threshold (e.g., 2).

Also, in some embodiments, each indicator in the screen representsinformation regarding a noise in a sub-region of the image, and may begenerated by the processing unit. In particular, after the processingunit obtains an image, the processing unit may then determines a firstautocorrelation map for a first sub-region in the image; determine,using the surface fit module, a first surface fit for first scenecontent in the first sub-region; subtract, using the subtraction module,the first surface fit from the first autocorrelation map for the firstsub-region in the image to obtain a first residual map; and quantify,using the peak quantification module, a first noise in the firstresidual map. The quantified first noise may then be represented by afirst indicator in the screen. The processing unit may also determine asecond autocorrelation map for a second sub-region in the image;determine a second surface fit for second scene content in the secondsub-region; subtract the second surface fit from the secondautocorrelation map for the second sub-region in the image to obtain asecond residual map; and quantify a second noise in the second residualmap. The quantified second noise may then be represented by a secondindicator in the screen.

In the above example, the information regarding the noise is presentedas an indicator that is in a form of a dot. In other embodiments, theinformation regarding the noise may be presented in other forms. Forexample, in other embodiments, the information regarding the noise maybe presented in the form of texts, such as a number indicating theactual level of the determined noise in each of the sub-regions in theimage. Also, the information may also include a level of confidenceassociated with the determined noise in each of the sub-regions in theimage. In some embodiments, the result may be written to a log file(e.g., a radiation machine performance check log file) which is updatedat specified time intervals.

In some embodiments, a processing unit (e.g., the processing unit 54 oranother processing unit) may be configured to determine a temporalaverage of N number of image frames.

In some cases, the determined noise as a function of degradation timemay be provided for a user. FIG. 7 illustrates an example of noise as afunction of degradation time for a camera exposed to radiation. Inparticular, the noise is represented as speckle noise RMS values in they-axis (e.g., counts/256 for a gray scale image). The x-axis representsdegradation time. The figure also shows the number of sub-regions wherenoise can be measured with high confidence. As shown in the figure, whena camera is exposed to radiation, the noise RMS increases over time.FIG. 8 illustrates noise as a function of degradation time for a cameranot exposed to radiation. This figure also shows the number ofsub-regions where noise can be measured with high confidence. As shownin the figure, when a camera is not exposed to radiation, the noise RMSstays relatively constant over time.

As shown in FIGS. 7 and 8, the relatively constant number of sub-regions(from which noise measurement may be determined with high confidence)over time may serve as a validation of the noise measurement.

As shown in FIGS. 7 and 8, the processing unit may be configured todetermine multiple information regarding a noise in a sub-region overtime, to thereby determine how the degradation of the camera occurs overtime. In particular, the processing unit may obtain a first image at afirst time. The processing unit may then determine a firstautocorrelation map for a first sub-region in the first image;determine, using the surface fit module, a first surface fit for firstscene content in the first sub-region; subtract, using the subtractionmodule, the first surface fit from the first autocorrelation map for thefirst sub-region in the first image to obtain a first residual map; andquantify, using the peak quantification module, a first noise in thefirst residual map. Later, at a different time, the processing unit mayobtain an additional (second) image. The processing unit may thendetermine a second autocorrelation map for a second sub-region in theadditional image; determine a second surface fit for second scenecontent in the second sub-region; subtracting the second surface fitfrom the second autocorrelation map for the second sub-region in theimage to obtain a second residual map; and quantifying a second noise inthe second residual map. Thus, the first image and the additional imageare obtained at different respective times. Also, a position of thefirst sub-region with respect to the image is a same as a position ofthe second sub-region with respect to the additional image. After thequantified first noise and the quantified second noise is obtained, theymay be output by the processing unit for presentation as a function oftime.

As shown in the above embodiments, the system and method describedherein are advantageous because they allow for in-service detection andquantification of fixed pattern speckle noise in video camera.

Although the above embodiments have been described with reference todetecting damage and/or deterioration in camera caused by radiation, inother embodiments, the technique and device described herein may beemployed to test any camera (e.g., camera for medical use, or camera fornon-medical use), which may or may not be exposed to any radiation. Forexamples, in other embodiments, the technique and device describedherein may be used to test and/or monitor cameras in nuclear researchand power plant facilities.

Processing System Architecture

FIG. 9 is a block diagram illustrating an embodiment of a specializedprocessing system 1600 that can be used to implement various embodimentsdescribed herein. For example, the processing system 1600 may beconfigured to implement the method of FIG. 3 in accordance with someembodiments. Also, in some embodiments, the processing system 1600 maybe used to implement the processing unit 54 of FIG. 1. The processingsystem 1600 includes a bus 1602 or other communication mechanism forcommunicating information, and a processor 1604 coupled with the bus1602 for processing information. The processor 1604 may be an example ofthe processor 54 of FIG. 1, or an example of any processor describedherein. The processing system 1600 also includes a main memory 1606,such as a random access memory (RAM) or other dynamic storage device,coupled to the bus 1602 for storing information and instructions to beexecuted by the processor 1604. The main memory 1606 also may be usedfor storing temporary variables or other intermediate information duringexecution of instructions to be executed by the processor 1604. Theprocessing system 1600 further includes a read only memory (ROM) 1608 orother static storage device coupled to the bus 1602 for storing staticinformation and instructions for the processor 1604. A data storagedevice 1610, such as a magnetic disk or optical disk, is provided andcoupled to the bus 1602 for storing information and instructions.

The processing system 1600 may be coupled via the bus 1602 to a display167, such as a cathode ray tube (CRT), for displaying information to auser. An input device 1614, including alphanumeric and other keys, iscoupled to the bus 1602 for communicating information and commandselections to processor 1604. Another type of user input device iscursor control 1616, such as a mouse, a trackball, or cursor directionkeys for communicating direction information and command selections toprocessor 1604 and for controlling cursor movement on display 167. Thisinput device typically has two degrees of freedom in two axes, a firstaxis (e.g., x) and a second axis (e.g., y), that allows the device tospecify positions in a plane.

In some embodiments, the processing system 1600 can be used to performvarious functions described herein. According to some embodiments, suchuse is provided by processing system 1600 in response to processor 1604executing one or more sequences of one or more instructions contained inthe main memory 1606. Those skilled in the art will know how to preparesuch instructions based on the functions and methods described herein.Such instructions may be read into the main memory 1606 from anotherprocessor-readable medium, such as storage device 1610. Execution of thesequences of instructions contained in the main memory 1606 causes theprocessor 1604 to perform the process steps described herein. One ormore processors in a multi-processing arrangement may also be employedto execute the sequences of instructions contained in the main memory1606. In alternative embodiments, hard-wired circuitry may be used inplace of or in combination with software instructions to implement thevarious embodiments described herein. Thus, embodiments are not limitedto any specific combination of hardware circuitry and software.

The term “processor-readable medium” as used herein refers to any mediumthat participates in providing instructions to the processor 1604 forexecution. Such a medium may take many forms, including but not limitedto, non-volatile media, volatile media, and transmission media.Non-volatile media includes, for example, optical or magnetic disks,such as the storage device 1610. A non-volatile medium may be consideredan example of non-transitory medium. Volatile media includes dynamicmemory, such as the main memory 1606. A volatile medium may beconsidered an example of non-transitory medium. Transmission mediaincludes coaxial cables, copper wire and fiber optics, including thewires that comprise the bus 1602. Transmission media can also take theform of acoustic or light waves, such as those generated during radiowave and infrared data communications.

Common forms of processor-readable media include, for example, a floppydisk, a flexible disk, hard disk, magnetic tape, or any other magneticmedium, a CD-ROM, any other optical medium, punch cards, paper tape, anyother physical medium with patterns of holes, a RAM, a PROM, and EPROM,a FLASH-EPROM, any other memory chip or cartridge, a carrier wave asdescribed hereinafter, or any other medium from which a processor canread.

Various forms of processor-readable media may be involved in carryingone or more sequences of one or more instructions to the processor 1604for execution. For example, the instructions may initially be carried ona magnetic disk of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over atelephone line using a modem. A modem local to the processing system1600 can receive the data on the telephone line and use an infraredtransmitter to convert the data to an infrared signal. An infrareddetector coupled to the bus 1602 can receive the data carried in theinfrared signal and place the data on the bus 1602. The bus 1602 carriesthe data to the main memory 1606, from which the processor 1604retrieves and executes the instructions. The instructions received bythe main memory 1606 may optionally be stored on the storage device 1610either before or after execution by the processor 1604.

The processing system 1600 also includes a communication interface 1618coupled to the bus 1602. The communication interface 1618 provides atwo-way data communication coupling to a network link 1620 that isconnected to a local network 1622. For example, the communicationinterface 1618 may be an integrated services digital network (ISDN) cardor a modem to provide a data communication connection to a correspondingtype of telephone line. As another example, the communication interface1618 may be a local area network (LAN) card to provide a datacommunication connection to a compatible LAN. Wireless links may also beimplemented. In any such implementation, the communication interface1618 sends and receives electrical, electromagnetic or optical signalsthat carry data streams representing various types of information.

The network link 1620 typically provides data communication through oneor more networks to other devices. For example, the network link 1620may provide a connection through local network 1622 to a host computer1624 or to equipment 1626 such as a radiation beam source or a switchoperatively coupled to a radiation beam source. The data streamstransported over the network link 1620 can comprise electrical,electromagnetic or optical signals. The signals through the variousnetworks and the signals on the network link 1620 and through thecommunication interface 1618, which carry data to and from theprocessing system 1600, are exemplary forms of carrier wavestransporting the information. The processing system 1600 can sendmessages and receive data, including program code, through thenetwork(s), the network link 1620, and the communication interface 1618.

Although particular embodiments have been shown and described, it willbe understood that it is not intended to limit the claimed inventions tothe preferred embodiments, and it will be obvious to those skilled inthe art that various changes and modifications may be made withoutdepartment from the spirit and scope of the claimed inventions. Thespecification and drawings are, accordingly, to be regarded in anillustrative rather than restrictive sense. The claimed inventions areintended to cover alternatives, modifications, and equivalents.

1. A method of detecting camera defect, comprising: obtaining an imageby a processing unit, the processing unit having a surface fit module, asubtraction module, and a peak quantification module, wherein the imageis generated using a camera; determining a first autocorrelation map fora first sub-region in the image; determining, using the surface fitmodule, a first surface fit for first scene content in the firstsub-region; subtracting, using the subtraction module, the first surfacefit from the first autocorrelation map for the first sub-region in theimage to obtain a first residual map; quantifying, using the peakquantification module, a first noise in the first residual map; andproviding an indication based on a result from the act of quantifyingthe first noise to indicate whether the camera is defective or not. 2.The method of claim 1, wherein the act of determining the surface fitcomprises determining a planar surface that represents the first scenecontent.
 3. The method of claim 1, wherein the act of determining thesurface fit comprises determining a non-planar surface that representsthe first scene content.
 4. The method of claim 1, wherein the act ofquantifying the first noise comprises determining whether a magnitude ofthe first noise exceeds a threshold or not.
 5. The method of claim 1,further comprising determining a level of confidence associated with thequantified first noise.
 6. The method of claim 5, wherein the act ofdetermining the level of confidence comprises determining a standarddeviation associated with a residual sidelobe in the first residual map.7. The method of claim 1, further comprising displaying the image in ascreen, and displaying an indicator representing the quantified firstnoise in the screen.
 8. The method of claim 7, wherein the indicator hasa first color if the quantified first noise is above a threshold, and asecond color if the quantified first noise is below the threshold. 9.The method of claim 7, wherein: the indicator has a first color if thequantified first noise is above a threshold, and the quantified firstnoise is determined with confidence, the indicator has a second color ifthe quantified first noise is below the threshold, and the quantifiedfirst noise is determined with confidence, and the indicator has a thirdcolor if the quantified first noise cannot be determined withconfidence.
 10. The method of claim 7, wherein the indicator isdisplayed over the image, and is at a location that corresponds with aposition of the sub-region in the image.
 11. The method of claim 7,wherein the indicator comprises a dot.
 12. The method of claim 1,further comprising: determining a second autocorrelation map for asecond sub-region in the image; determining a second surface fit forsecond scene content in the second sub-region; subtracting the secondsurface fit from the second autocorrelation map for the secondsub-region in the image to obtain a second residual map; and quantifyinga second noise in the second residual map.
 13. The method of claim 1,further comprising: obtaining an additional image; determining a secondautocorrelation map for a second sub-region in the additional image;determining a second surface fit for second scene content in the secondsub-region; subtracting the second surface fit from the secondautocorrelation map for the second sub-region in the image to obtain asecond residual map; and quantifying a second noise in the secondresidual map; wherein the first image and the additional image areobtained at different respective times; and wherein a position of thefirst sub-region with respect to the image is a same as a position ofthe second sub-region with respect to the additional image.
 14. Themethod of claim 13, further comprising presenting the quantified firstnoise and the quantified second noise as a function of time.
 15. Anapparatus for detecting camera defect, comprising: a processing unithaving a surface fit module, a subtraction module, and a peakquantification module; wherein the processing unit is configured toobtain an image generated using a camera, and determining a firstautocorrelation map for a first sub-region in the image; wherein thesurface fit module is configured to determine a first surface fit forfirst scene content in the first sub-region; wherein the subtractionmodule is configured to subtract the first surface fit from the firstautocorrelation map for the first sub-region in the image to obtain afirst residual map; wherein the peak quantification module is configuredto quantify a first noise in the first residual map; and wherein theprocessing unit also comprises an indication generator for providing anindication based on a result from the quantified first noise to indicatewhether the camera is defective or not.
 16. The apparatus of claim 15,wherein the surface fit module is configured to determine the surfacefit by determining a planar surface that represents the first scenecontent.
 17. The apparatus of claim 15, wherein the surface fit moduleis configured to determine the surface fit by determining a non-planarsurface that represents the first scene content.
 18. The apparatus ofclaim 15, wherein the peak quantification module is configured toquantify the first noise by determining whether a magnitude of the firstnoise exceeds a threshold or not.
 19. The apparatus of claim 15, furthercomprising a confidence level module configured to determine a level ofconfidence associated with the quantified first noise.
 20. The apparatusof claim 19, wherein the confidence level module is configured todetermine the level of confidence by determining a standard deviationassociated with a residual sidelobe in the first residual map.
 21. Theapparatus of claim 15, further comprising a screen for displaying theimage, and for displaying an indicator representing the quantified firstnoise.
 22. The apparatus of claim 21, wherein the indicator has a firstcolor if the quantified first noise is above a threshold, and a secondcolor if the quantified first noise is below the threshold.
 23. Theapparatus of claim 21, wherein: the indicator has a first color if thequantified first noise is above a threshold, and the quantified firstnoise is determined with confidence, the indicator has a second color ifthe quantified first noise is below the threshold, and the quantifiedfirst noise is determined with confidence, and the indicator has a thirdcolor if the quantified first noise cannot be determined withconfidence.
 24. The apparatus of claim 21, wherein the screen isconfigured to display the indicator over the image at a location thatcorresponds with a position of the sub-region in the image.
 25. Theapparatus of claim 21, wherein the indicator comprises a dot.
 26. Theapparatus of claim 15, wherein the processing unit is configured todetermine a second autocorrelation map for a second sub-region in theimage; wherein the surface fit module is configured to determine asecond surface fit for second scene content in the second sub-region;wherein the subtraction module is configured to subtract the secondsurface fit from the second autocorrelation map for the secondsub-region in the image to obtain a second residual map; and wherein thepeak quantification module is configured to quantify a second noise inthe second residual map.
 27. The apparatus of claim 15, wherein theprocessing unit is configured to obtain an additional image, anddetermine a second autocorrelation map for a second sub-region in theadditional image; wherein the surface module is configured to determinea second surface fit for second scene content in the second sub-region;wherein the subtraction module is configured to subtract the secondsurface fit from the second autocorrelation map for the secondsub-region in the image to obtain a second residual map; and wherein thepeak quantification module is configured to quantify a second noise inthe second residual map; wherein the first image and the additionalimage are obtained at different respective times; and wherein a positionof the first sub-region with respect to the image is a same as aposition of the second sub-region with respect to the additional image.28. The apparatus of claim 27, further comprising a screen forpresenting the quantified first noise and the quantified second noise asa function of time.
 29. A processor-program product having a set ofinstruction stored in a processor-readable non-transitory medium, anexecution of which by a processing unit causes a method of detectingcamera defect to be performed, the method comprising: obtaining an imagegenerated using a camera; determining a first autocorrelation map for afirst sub-region in the image; determining, using a surface fit module,a first surface fit for first scene content in the first sub-region;subtracting, using a subtraction module, the first surface fit from thefirst autocorrelation map for the first sub-region in the image toobtain a first residual map; quantifying, using a peak quantificationmodule, a first noise in the first residual map; and providing anindication based on a result from the act of quantifying the first noiseto indicate whether the camera is defective or not.